A joint clutter suppression method based on robust principal component analysis and total variation denoising

被引:2
|
作者
Xu, Bangzhen [1 ]
Lu, Xingyu [1 ]
Gu, Hong [1 ]
Su, Weimin [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Principal component analysis;
D O I
10.1049/ell2.12399
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the scene of ground moving target detection, the target echo information is often corrupted by strong clutter. In this letter, a joint clutter suppression algorithm is proposed based on robust principal component analysis and total variation denoising for pulse Doppler radar. The novel method named total variation- robust principal component analysis replaces the l1-norm in robust principal component analysis algorithm with a total variation seminorm. Total variation robust principal component analysis retains the clutter suppression ability of robust principal component analysis, and effectively separates the target from the noise in range-Doppler domain.
引用
收藏
页码:213 / 215
页数:3
相关论文
共 50 条
  • [1] The Multi-domain Union Clutter Suppression Algorithm Based on Robust Principal Component Analysis
    Li Xiangping
    Wang Mingze
    Dan Bo
    Li Wei
    Ma Junwei
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2022, 44 (04) : 1303 - 1310
  • [2] Fringe pattern denoising based on robust principal component analysis
    Zhang, Yiwei
    Xi, Jiangtao
    Tong, Jun
    Yu, Yanguang
    Guo, Qinghua
    DIMENSIONAL OPTICAL METROLOGY AND INSPECTION FOR PRACTICAL APPLICATIONS X, 2021, 11732
  • [3] Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total variation
    Zhang, Yan
    Shao, Yuyi
    Shen, Jinyue
    Lu, Yao
    Zheng, Zhouzhou
    Sidib, Yaya
    Yu, Bin
    APPLIED OPTICS, 2021, 60 (16) : 4916 - 4929
  • [4] Manifold Denoising by Nonlinear Robust Principal Component Analysis
    Lyu, He
    Sha, Ningyu
    Qin, Shuyang
    Yan, Ming
    Xie, Yuying
    Wang, Rongrong
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [5] Reverberation clutter signal suppression in ultrasound attenuation estimation using wavelet-based robust principal component analysis
    Lok, U-Wai
    Gong, Ping
    Huang, Chengwu
    Tang, Shanshan
    Zhou, Chenyun
    Yang, Lulu
    Watt, Kymberly D.
    Callstrom, Matthew
    Trzasko, Joshua D.
    Chen, Shigao
    PHYSICS IN MEDICINE AND BIOLOGY, 2022, 67 (09):
  • [6] Video denoising and moving object detection by rank-1 and total variation regularization on robust principal component analysis framework
    Yang, Guoliang
    Yu, Dingling
    Wen, Junlin
    Lin, Jianbin
    Liang, Liming
    JOURNAL OF ELECTRONIC IMAGING, 2020, 29 (03)
  • [7] Robust principal component analysis combined with top-hat transform for clutter suppression in GPR images
    Ye, Fang
    Zhang, Rui
    Liu, Ziran
    REMOTE SENSING LETTERS, 2025, 16 (01) : 66 - 76
  • [8] Joint principal component analysis and total variation for infrared and visible image fusion
    Zhang, Xuefeng
    Dai, Xiaobing
    Zhang, Xuemin
    Jin, Guang
    INFRARED PHYSICS & TECHNOLOGY, 2023, 128
  • [9] A clutter suppression method based on improved principal component selection rule for ground penetrating radar
    Zhu J.
    Xue W.
    Rong X.
    Yu Y.
    Progress In Electromagnetics Research M, 2017, 53 : 29 - 39
  • [10] Point cloud denoising using robust Principal Component Analysis
    Narvaez, Esmelde A. Leal
    Narvaez, Nallig Eduardo Leal
    GRAPP 2006: PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS THEORY AND APPLICATIONS, 2006, : 51 - +